Very often you will need to write a process or function that can
flexibly handle incoming data, and can manage variables that may or may
not exist. One way would be to write a bunch of
if(exists("variable", dataset)){ } type structures, where
the operations are in the curly brackets. There are better
approaches.
We can use the rename() function in combination with the
any_of() selection helper function. The way it works is
that the new-name and old-name pairs are passed as a vector to the
any_of selection helper within rename. An
example of how it works is shown below. The variable
Species does exist and is renamed to
Type. The variable Scent does
not exist, and nothing is renamed to Smell.
library(tidyverse)
new_dataset <- iris %>%
rename(any_of(c("Type" = "Species", "Smell" = "Scent")) )
head(new_dataset)## Sepal.Length Sepal.Width Petal.Length Petal.Width Type
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaAgain we make use of the selection helper function
any_of(), this time inside the function
across(), which itself is inside the function
arrange(). Again the variable names (which may or may not
exist) are passed as a character vector. In the example below, the
variable cyl does exist, while the
variable emissions.
library(tidyverse)
new_dataset <- mpg %>%
arrange(across(any_of(c('cyl' , 'emissions'))))
head(new_dataset)## # A tibble: 6 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manua… f 21 29 p comp…
## 3 audi a4 2 2008 4 manua… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto(… f 21 30 p comp…
## 5 audi a4 quattro 1.8 1999 4 manua… 4 18 26 p comp…
## 6 audi a4 quattro 1.8 1999 4 auto(… 4 16 25 p comp…